Live-cell computed tomography

ABSTRACT

Systems and methods of using the same for functional fluorescence imaging of live cells in suspension with isotropic three dimensional (3D) diffraction-limited spatial resolution are disclosed. The method-live cell computed tomography (LCCT)-in-volves the acquisition of a series of two dimensional (2D) pseudo-projection images from different perspectives of the cell that rotates around an axis that is perpendicular to the optical axis of the imaging system. The volumetric image of the cell is then tomographically reconstructed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a U.S. National Stage entry of International Application PCT/US2018/020711, filed on Mar. 2, 2018 and claims priority to U.S. Provisional Patent Application No. 62/466,306, filed on Mar. 2, 2017, the disclosures of which is are incorporated herein by reference.

TECHNICAL FIELD

This present application relates to a computed tomography technology, more particularly, to computed tomography technology applied to a live or fixed cell, multicellular cluster, or tissue in suspension in the fields of clinical medicine and biological research.

BACKGROUND

Live cell imaging has advanced significantly and the field has propelled into the next cycle of application-driven developments for addressing cell biology questions that were previously unapproachable. Understanding the internal organization of biological cells, inherently 3D structures, is pivotal for the analysis of cellular function and response to external stimuli and stress. Despite the significant advances in 3D imaging of live cells, only a few methods offer nearly isotropic 3D spatial resolution. Furthermore, the majority of these imaging modalities require the cell to be immobilized during imaging, which may compromise biological dynamics when imaging natural suspension cells, e.g. immune system cells. The cellular microenvironment has been shown to be crucial for cell function, homeostasis and pathogenesis. Consequently, studies focusing on the immune system that rely on artificial immobilization of cells on substrates may alter cellular organization and affect signaling pathways, since the natural state of these cells is in liquid suspension or non-adherent in tissues.

While X-ray tomography has been in clinical use since the 1970s, optical tomography at the cellular level is relatively new. There is a need for technologies that image cell structure and function in three dimensions with true isotropic resolution (e.g., for detecting many diseases including cancer, drug screening, and in elucidating basic biological mechanisms).

SUMMARY

In one or more embodiments, provided herein is a Live-Cell Computed Tomography (LCCT) system that samples and images an individual live cell, individual fixed cell, live multicellular clusters, fixed multicellular clusters, live tissue, or fixed tissue in suspension in a module that can be attached to a commercial inverted microscope. While the cell(s) (or cluster(s)) or tissue is/are rotated “free in medium” by one of several means, or rotated in a biocompatible gel in acapillary imaging chamber, image projections or other transmission or emission data are acquired from a plurality of angular perspectives by an objective lens-camera combination. In certain embodiments, the imaging chamber is a capillary. A three dimensional image with isotropic spatial resolution of the cell is synthesized or reconstructed by means of a series of computer algorithms, producing quantitative biosignatures useful for disease diagnosis, drug screening, and biological studies.

Embodiments of the technology disclosed herein include an optical computed tomography system for acquiring three dimensional (3D) images. The system comprises a frame having a first plane comprising a first axis and a second plane comprising a second axis, wherein the first plane is substantially orthogonal to the second plane and the first axis is substantially orthogonal to the second axis; a device manifold supported by said frame and disposed parallel to said first plane; and a first optical train connected to said frame and disposed along said second axis. Further, in certain embodiments, the system comprises a second optical train connected to said frame and disposed along said second axis. Moreover, in certain embodiments, the system further comprises a scanning objective, a camera, and a light delivery module.

In certain embodiments, the frame further comprises a platform that is able to move along the first axis, the second axis, and a third axis, wherein the third axis is disposed with the first plane and is substantially transverse to the first axis. The device manifold further comprises a microfluidic module configured to comprise an object rotation region and at least one imaging chamber. Moreover, the device manifold further comprises an object dispensing region, which comprises a fluid pumping apparatus comprising an air pressure controller, a vacuum controller, a fluid reservoir, and a flow monitoring device.

Embodiments of the technology disclosed herein also include a method of rotating an object. The method comprises generating a rotating electric field; inducing an electric dipole moment in the object by the rotating electric field; and rotating the object at a speed by the rotating electric field. In certain embodiments, the object is selected from the group consisting of a live cell, live multicellular cluster, fixed cell, fixed multicellular cluster, live tissue, fixed tissue, and any combinations thereof.

In addition, embodiments of the technology disclosed herein include a method of acquiring a 3D image of an object. The method comprises providing an imaging chamber containing an object and a microscope, wherein the chamber can rotate around using electrical fields as disclosed herein a first axis when a focal plane of the microscope scans through the chamber perpendicularly to the first axis; rotating the chamber containing the object; acquiring a two dimensional (2D) pseudoprojection (PP) image of the object during rotation to form a plurality of N (N>1) 2D PP images of the object; correcting the background of the plurality of N (N>1) 2D PP images of the object to generate a plurality of M (M=N) refined 2D PP images of the object; and aligning the plurality of M refined 2D PP images of the object to construct a 3D image of the object. Each 2D PP image of the object corresponds to a rotation angle of the object at an angular sampling rate. In certain embodiments, the imaging chamber is a capillary.

Further, the correcting step further comprises generating a plurality of X (X=N) binary 2D PP images of the object; removing the object from the chamber and acquiring a plurality of Y (Y=N) projections of the chamber during the rotation of the chamber, wherein each of the plurality of Y projections of the chamber corresponds to each of the plurality of N 2D PP images of the object at a particular angle; evaluating intensity of each of the plurality of Y projections of the chamber to form a plurality of Z (Z=Y) background intensity; removing each corresponding background intensity from each of the plurality of X binary 2D PP images to form a plurality of J (J=X) corrected binary 2D PP images of the object; and refining the plurality of J corrected binary 2D PP images of the object to form the plurality of M (M=J) refined 2D PP images of the object. In some embodiments, the removing step comprises subtracting each corresponding background intensity from each of the plurality of X binary 2D PP images to form a plurality of J (J=X) corrected binary 2D PP images of the object. In other embodiments, the removing step comprises dividing each of the plurality of X binary 2D PP images by each corresponding background intensity to form a plurality of J (J=X) corrected binary 2D PP images of the object.

In some embodiments, the refining step comprises employing a total variation noise reduction (TV) method. In other embodiments, the refining step comprises employing a filtering method that is a SAFIR filter. In yet other embodiments, the refining step comprises employing a filtering method that is a block machine filtering (BM3D) filter.

Moreover, in certain embodiments, the aligning step further comprises determining a center of the object on each of the plurality of M refined 2D PP images of the object; determining a size of the object on each of the plurality of M refined 2D PP images of the object; cutting the object from each of the plurality of M refined 2D PP images of the object; reconstructing the cut object with another cut object from a forward refined 2D PP image of the object; and repeating the reconstruction step until generating a 3D model of the object.

Embodiments of the technology disclosed herein also describes a non-transitory computer useable medium encoded with a computer program product to acquire a 3D image of an object and useable with programmable computer processor disposed within a controller in communication with an assembly comprising an imaging chamber containing the object and a microscope. In certain embodiments, computer readable medium comprises a magnetic storage medium, an optical storage medium, or an electronic storage medium. Electronic storage medium means a PROM, EPROM, EEPROM, Flash PROM, compactflash, smartmedia, and the like.

In certain embodiments, the computer program product comprises computer readable program code that causes said programmable computer processor to rotate the chamber containing the object, computer readable program code that causes said programmable computer processor to acquire a two dimensional (2D) pseudoprojection (PP) image of the object during rotation to form a plurality of N (N>1) 2D PP images of the object, computer readable program code that causes said programmable computer processor to correct the background of the plurality of N (N>1) 2D PP images of the object to generate a plurality of M (M=N) refined 2D PP images of the object, and computer readable program code that causes said programmable computer processor to align the plurality of M refined 2D PP images of the object to construct a 3D image of the object.

Further, in certain embodiments, the computer program product comprises computer readable program code that causes said programmable computer processor to generate a plurality of X (X=N) binary 2D PP images of the object; computer readable program code that causes said programmable computer processor to remove the object from the chamber and acquiring a plurality of Y (Y=N) projections of the chamber during the rotation of the chamber, wherein each of the plurality of Y projections of the chamber corresponds to each of the plurality of N 2D PP images of the object at a particular angle; computer readable program code that causes said programmable computer processor to evaluate intensity of each of the plurality of Y projections of the chamber to form a plurality of Z (Z=Y) background intensity; computer readable program code that causes said programmable computer processor to remove each corresponding background intensity from each of the plurality of X binary 2D PP images to form a plurality of J (J=X) corrected binary 2D PP images of the object; and computer readable program code that causes said programmable computer processor to refine the plurality of J corrected binary 2D PP images of the object to form the plurality of M (M=J) refined 2D PP images of the object.

In some embodiments, the computer program product contains computer readable program code that causes said programmable computer processor to employ a total variation noise reduction (TV) method. In other embodiments, the computer program product contains computer readable program code that causes said programmable computer processor to employ a SAFIR filter. In yet other embodiments, the computer program product contains computer readable program code that causes said programmable computer processor to employ a block machine filtering (BM3D) filter.

Further, in certain embodiments, the computer program product includes computer readable program code that causes said programmable computer processor to determine a center of the object on each of the plurality of M refined 2D PP images of the object; computer readable program code that causes said programmable computer processor to determine a size of the object on each of the plurality of M refined 2D PP images of the object; computer readable program code that causes said programmable computer processor to cut the object from each of the plurality of M refined 2D PP images of the object; computer readable program code that causes said programmable computer processor to reconstruct the cut object with another cut object from a forward refined 2D PP image of the object; and computer readable program code that causes said programmable computer processor to repeat the reconstruction step until generating a 3D model of the object.

BRIEF DESCRIPTION OF THE DRAWINGS

The technology disclosed herein will be better understood from a reading of the following detailed description taken in conjunction with the drawings in which like reference designators are used to designate like elements, and in which:

FIG. 1 illustrates one embodiment of a Live-Cell Computed Tomography (LCCT) instrument frame with three major subcomponents shown;

FIG. 2A depicts volumetric 3D image reconstruction;

FIG. 2B illustrates one embodiment of a primary optical train schematic

FIG. 2C depicts a 2D pseudo-projection image;

FIGS. 3A and 3B illustrates one embodiment of a secondary optical train schematic and model;

FIG. 4 illustrates one embodiment of a device manifold;

FIG. 5 illustrates one embodiment of cell rotation;

FIG. 6A illustrates one embodiment of delivering an object from a cell staging area to an imaging chamber in a fluid manner;

FIG. 6B illustrates pseudoprojection (PP) formation with the Cell-CT. Cells flow through a 50-micron capillary into the field of view of a high NA objective. The cell is then rotated as the focal plane is swept through the entire cell to form a single PP image at each of hundreds of angles.

FIG. 7A shows that projection images obtained at four orientations of the cell during a full rotation in the electrocage device, Scale bar: 5 microns;

FIGS. 7B and 7C show lateral shift of the cell along X and Y axis during one full rotation and rotation rate stability;

FIG. 7D shows spatial resolution characterization using 200 nm fluorescent beads. Beads were internalized by the cells prior to experiment. Images of one such bead are shown in XY and XZ direction;

FIGS. 7E and 7F shows lateral spatial resolution;

FIG. 7G demonstrates the advantage of the LCCT approach of distortion-free, orientation independent 3D imaging due to isotropic spatial resolution;

FIG. 8A illustrates MIP images at three orthogonal orientations (grayscale) and volume rendering (color, lower right) of the nuclear DNA stained with Hoechst 33342. Lighter regions in the MIP images correspond to higher density of the DNA. A fold-like feature can be seen in the volume rendering (arrow);

FIG. 8B shows MIP images of the mitochondria of the same cell as in FIG. 8A stained with the mitochondrial marker MitoTracker Red. A volume rendering (lower right panel) indicates some of the mitochondria co-localized with the fold in the nucleus in FIG. 8A;

FIG. 8C is an overlay of the volume renderings of the nuclear and mitochondrial data shows some of the mitochondria (arrow) aligned with the fold in the nucleus in FIG. 8A;

FIG. 8D is a cut-away presentation of the overlay reveals mitochondria located deep in the nuclear fold;

FIGS. 8E and 8F are partial segmentation of the bright (70-100% range of the intensity histogram) nuclear and mitochondrial structures, correspondingly. Using an intensity threshold above 70% of the camera detection dynamic range highlights the brightest features of the two organelles. Both data sets—the nucleus and mitochondria—were acquired simultaneously. Both sets of data were deconvolved using a Gaussian point spread function. Scale bar: 5 μm;

FIG. 8G shows mitochondria of the same cell presented as a series of slices through the cell at different depths (left panel). The contrast was enhanced in the intensity images to make less intense structures visible. The panel on the right shows segmentation of the mitochondria using the Niblack local threshold approach. The segmentation results were smoothed to enhance visibility. Scale bar: 5 μm;

FIG. 8H is a 3D surface rendering of the mitochondrial network of the same cell with fluorescence intensity and segmented mitochondria shown in gray and orange, respectively;

FIGS. 8I and 8J shows mitochondria fluorescence intensity (I, surface rendering) and the corresponding segmentation result (J) of a selected small region of interest of the same cell;

FIG. 9A is a comparison of an untreated J774A.1 cell (left) and one treated with 8-bromo-cAMP (right). MIP (upper row) and isosurface rendering (bottom row) show a marked shift towards larger structures;

FIG. 9B shows quantitative analysis of the mitochondrial structure volumes corroborate the qualitative findings of FIG. 9A showing the appearance of structures with larger volume in the treated cell;

FIG. 9C is a comparison of the average values of the volume distribution demonstrates statistically significant (p=0.001, Kolmogorov-Smirnov test, increase in the volume of the structures after treatment;

FIG. 9D shows combined mitochondrial structures volume distribution of 30 control and 30 treated cells. A trend toward increasing volume of the structures can be seen;

FIG. 9E shows the average values of the volume distribution shows less pronounced, but statistically significant (p=0.008, Kolmogorov-Smirnov test) increase in the mitochondrial volume in treated vs. control cells.

FIG. 10A shows alterations in the nuclear DNA spatial distribution. The graph shows changes in the volume distribution over time, revealing most dynamics in the structures with larger (2-12 fL) volumes, whereas the smaller features show little change;

FIG. 10B demonstrated mitochondrial structure dynamics, which showed qualitatively similar behavior as the nucleus. The majority of changes can be seen in the structures with larger (3-20 fL) volumes, while the smaller structures show relatively little alteration;

FIG. 11 is a flowchart illustrating one embodiment of the method of acquiring a plurality of 2D images of an object and constructing a 3D image based on the plurality of the 2D images;

FIG. 12 are example pseudoprojections taken in absorption mode at 0° and 180° angles. The capillary edges are vertical near the edges of the projections;

FIG. 13 illustrates the creation of the approximate background. All the pseudo-projections are used to create the background by masking the location of the cells and the debris and removal of the intensity profile due to the capillary. The final background is the average of all the pseudoprojection backgrounds;

FIGS. 14A-G: (A) Raw projection with pixel variation and dust artifacts. Projection corrected with (B) standard flood field background subtraction; (C) masked backgrounds; (D) Total Variation (TV) refined background; (E) SAFIR filtering; and (F) BM3D filtering. (G) Quality of the various techniques presented as the variation of a spatial patch away from the cell on all the PPs in a full rotation series. (The cell intensity on this scale is 10,000 to 30,000);

FIG. 15 shows comparison of reconstructions produced using the various registration methods. All panels used TV background correction. (A) COG (B) mCOG. (C) CC (D) nCOG (E) tCC. (F) Comparison of the various registration methods by use of the correlation of the pseudoprojection from one angle with its 180° mirror;

FIG. 16 demonstrates reconstructions using various background filters after correcting for the capillary. (A) Total variation (TV) followed by median smoothing; (B) BM3D filter; and (C) SAFIR filter.

FIG. 17: center of Gravity registered cell is then distorted to distribute the inherent noise, and the center of gravity routine is run again to provide a fine alignment of the image. Iteratively, the crudely registered cell projection (A) is contaminated with additive noise (B) to “wash out” the inherent noise. The center of gravity is computed again (C) to provide a fine alignment of the cell projection; and

FIG. 18: a rough filtered back projection recontruction is made and then templated registration is used to precisely align all the pseudoprojections (right column) with its Maximum Intensity Forward Projections (MIP, left column);

DETAILED DESCRIPTION

The present application is directed to systems and methods of using the same for capturing a functional fluorescence 3D image of a cell, multicellular cluster, or tissue in suspension, more particularly, a live cell, multicellular cluster, or tissue with isotropic 3D diffraction-limited spatial resolution. Optical computed tomography (CT) imaging of a single cell(s) (and cell cluster(s) and tissue) is made possible by recent developments in low-light level imaging and other detectors, microelectronics, microfluidics and high-speed computing.

This technology disclosed herein is described in one or more exemplary embodiments in the following description with reference to the Figures, in which like numbers represent the same or similar elements. Reference throughout this specification to “one embodiment,” “an embodiment,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the technology disclosed herein. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

The described features, structures, or characteristics of the technology disclosed herein may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are recited to provide a thorough understanding of embodiments of the technology disclosed herein. One skilled in the relevant art will recognize, however, that the technology disclosed herein may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the technology disclosed herein.

The schematic flow chart diagrams included are generally set forth as a logical flow-chart diagram (e.g., FIG. 11). As such, the depicted order and labeled steps are indicative of one embodiment of the presented method. In certain embodiments, other steps and methods are conceived that are equivalent in function, logic, or effect to one or more steps, or portions thereof, of the illustrated method. Additionally, the format and symbols employed are provided to explain the logical steps of the method and are understood not to limit the scope of the method. Although various arrow types and line types are employed in the flow-chart diagrams, they are understood not to limit the scope of the corresponding method (e.g., FIG. 11). Indeed, some arrows or other connectors may be used to indicate only the logical flow of the method. For instance, an arrow indicates a waiting or monitoring period of unspecified duration between enumerated steps of the depicted method. Additionally, the order in which a particular method occurs may or may not strictly adhere to the order of the corresponding steps shown.

As used herein, “electric dipole moment” is defined as a measure of the separation of positive and negative electrical charges within a system, that is, a measure of the system's overall polarity. The electric field strength of the dipole is proportional to the magnitude of dipole moment. The SI units for electric dipole moment are Coulomb-meter (C m), however the most commonly used unit is the Debye (D). An electric dipole is defined by the first-order term of the multipole expansion, and consists of two equal and opposite charges infinitely close together.

“Spatial resolution” refers to the number of pixels utilized in construction of a digital image. Images having higher spatial resolution are composed with a greater number of pixels than those of lower spatial resolution.

“Point spread function (PSF)” describes the response of an imaging system to a point source or point object. A more general term for the PSF is a system's impulse response, the PSF being the impulse response of a focused optical system. The PSF in many contexts can be thought of as the extended blob in an image that represents an unresolved object. In functional terms it is the spatial domain version of the transfer function of the imaging system.

“Temporal resolution” refers to the precision of a measurement with respect to time. Often there is a trade-off between the temporal resolution of a measurement and its spatial resolution, due to Heisenberg's uncertainty principle.

Referring to FIG. 1, an embodiment of the Live-Cell Computed Tomography (LCCT) system is illustrated. In certain embodiments, the LCCT system 100 is built around a frame, which is a microscope as shown in FIG. 1. In certain embodiments, the frame 108 comprises three major physical subcomponents: a first (primary) optical train 200, a second (secondary) optical train 300, and a device manifold 400. In other embodiments, the frame 108 comprises the first optical train 200 and the device manifold 400. Further, the frame 108 comprises a first plane comprising a first axis 102 and a second plane comprising a second axis 104. Moreover, the first plane comprises a third axis 106, which is substantially transverse to the first axis 102.

Referring to FIG. 2B, an embodiment of the first optical train 200 is illustrated. In certain embodiments, a light source 202 delivers a light via an internal optical path to a first dichroic mirror 208, which reflects the light to a scanning objective 206 along an optical axis 230. In some embodiments, the light source 202 delivers a laser light. In other embodiment, the light source 202 comprises a light-emitting diode (LED), which is a two-lead semiconductor light source and emits light when activated. The scanning objective 206 focuses on an object 220. In some embodiments, the object 220 is a single live cell or a single fixed cell. In other embodiments, the object 220 is a live or fixed multicellular cluster(s). In other embodiments, the object 220 is a live or fixed tissue. In yet other embodiments, the object 220 is a microparticle amenable to imaging with visible optical radiation.

Further, an emission signal generated by the object 220 travels along the optical axis 230 through the scanning objective 206 to a second dichroic mirror 210, which reflects the emission signal to a emission filter 212 onto a camera chip 216. The emission signal can be captured by a camera 204. In some embodiments, the camera 204 is an electron-multiplying charge-coupled device (EMCCD). In other embodiments, the camera 204 is a scientific complementary metal-oxide-semiconductor (sCMOS) camera.

Referring to FIGS. 3A and 3B, one embodiment of a secondary optical train 300 is illustrated. In some embodiments, the secondary optical train 300 is used for transmission mode imaging. In other embodiments, the secondary optical train 300 is used for optical trapping of the cells. In certain embodiments, the basic components of the secondary optical train 300 are similar to the first optical train 200, however the assembly is mounted directly opposite the primary optical train and on the same optical axis 230 (FIG. 2B). The secondary optical train 300 serves to deliver or receive transmitted light to and/or from the object 220. Referring to FIG. 3B, a laser beam delivered through a light source can act as an optical trap to confine or immobilize the object 220 for imaging.

Referring to FIG. 4, one embodiment of the device manifold 400 is illustrated. In certain embodiment, the device manifold 400 comprises a microfluidic module, which contains an object rotation region, or “imaging chamber”. In some embodiments, the device manifold 400 comprises one or at least one imaging chamber. In other embodiments, the device manifold 400 comprises a plurality of imaging chambers. In certain embodiments, the device manifold 400 further connects to at least one of a fluid driving system, light delivery modules, computers and other appendages.

Further, the device manifold 400 is mounted to an X-Y-Z motion platform in order to bring the object cell(s) precisely into the field of view. The device manifold allows for a simple loading and unloading of a microfluidic device which incorporates the imaging chamber. Moreover, the device manifold 400 interfaces fluid and electrical inputs between the system 100 and the microfluidic device. The device manifold 400 can have the ability to hold additional parts. As a non-limiting example, a series of PCR tubes can be hold by the device manifold 400.

The microfluidic device can be manufactured to effect any of several cellular rotation methods. Three methods of rotation are based upon: asymmetrical light pattern, hydrodynamic vortex, and dielectrophoretic torque (“electrocage”). Referring to FIG. 5, an embodiment of the microfluidic device 500 facilitates an electrocage 510 for rotating the object 220.

Furthermore, the microfluidic device incorporates means for delivering the object 220 to the optical axis 230 of the microscope. All methods may use fluid flow. In addition to fluid flow, one method utilizes optical tweezers, delivered via one of the optical trains, to trap the object 220 of interest and manipulate it through the microfluidic device to the optical axis 230. A second method utilizes a plurality of microelectrodes arranged along the microfluidic channel.

Referring to FIG. 11, in step 1105, the object 220 is delivered to the imaging chamber 500. In certain embodiments, a polysaccharide cell medium can be used to suspend the object 220 and support its life and normal function as it is delivered to the optical axis 230 and then rotated. In step 1110, the object 220 is immobilized. Further, in step 1115, the object 220 is rotated steadily along a first axis 240 of an electric field. The first axis 240 is substantially transverse to the optical axis 230.

Referring to FIG. 5, the imaging chamber 500 comprises a plurality of microelectrodes 520 a, 520 b, 522 a, 522 b, 524 a, 524 b, 526 a, and 526 b. Microelectrodes 520 a, 522 a, 524 a, and 526 a are located within one plane, which is directly above the other plane, within which microelectrodes 520 b, 522 b, 524 b, and 526 b are located. The electric current following through all the microelectrodes generates an electric field 510, which can also be called the electrocage. The electric field 510 further comprises a y-axis dipole 610 (FIG. 6). Also, the electric field 510 comprises an x-axis dipole 620 (FIG. 5), which cause the object 220 to rotate along the x-axis. The electric field 510 is a high frequency electric field with a rotating phase pattern in the 3D space. The cell rotation is a result of an induced dipole moment in the cell due to the rotation of the electric field that imposes a torque on the cell.

Moreover, in certain embodiments, the rotating electric field pattern is generated by applying a sinusoidal waveform at a frequency of 1-10 megahertz (MHz) and voltage (V) peak-to-peak amplitude to each of the microelectrodes with a phase offset by 90 degrees. In other embodiments, the sinusoidal waveform has a frequency of 1-2 MHz and 1-2 V peak-to-peak amplitude to each of the microelectrodes. In yet other embodiments, the electrical field frequency is about 0.5-2 MHz and the electrical field amplitude is about 2 V (peak-to-peak). In certain embodiments, using the electrocage 510, Applicants are able to consistently rotate live myelogenous leukemia cells (K562 cell line) and mouse macrophages (J774A.1 cell line) at stable speeds ranging between 1-3 rotations per minute (RPM). In other embodiments, the cell rotation speed is about 1-10 rotations per minute (RPM), e.g., 1.5 RPM, 2 RPM, 2.5 RPM, 3 RPM, 3.5 RPM, 4 RPM, 4.5 RPM, 5 RPM, 5.5 RPM, 6 RPM, 6.5 RPM, 7 RPM, 7.5RPM, 8 RPM, 8.5 RPM, 9 RPM, 9.5 RPM, 10 RPM, or another RPM from about 1 to about 10 RPM. As described herein, “about” is defined as plus or minus 10% difference in any measurement.

In addition, rotation stability of the object 220 is characterized by measuring lateral, in-plane shifts in the X and Y direction during the rotation of the object 220. While the lateral displacement of cells varied from cell to cell, it appeared to depend on the cell shape (or how far it deviated from an ideal spherical shape). Image analysis revealed a coefficient of variation, or relative lateral shift, from the initial (0°) cell position of 0.057 and 0.084 of the cell diameter in X and Y directions per full rotation of the cell, respectively (FIG. 7B). Such shifts take place over an entire rotation of the cell (20-60 seconds) and are relatively slow when compared with the time scale of an individual, one direction/half cycle scan pseudo-projection acquisition (40 ms). Consequently, adjusted to the 40 ms time scale these shifts can be ignored, as they become minor, resulting only in about 10⁻⁴ shift of the cell diameter (assuming 2 RPM or 30 seconds/rotation, online Materials and Methods) and do not distort pseudo-projection images appreciably. As a result, lateral shifts on this time and spatial scale can be reliably corrected computationally, using appropriate algorithms.

To fully characterize cell rotation stability, Applicants determined how stable the rotation rate is over multiple rotations of the same cell. To this end, Applicants calculated the coefficient of variation of the rotation rate using data of five cells with 5-7 full rotations each (FIG. 7C) The average coefficient of variation of the rotation rate per full rotation is 0.051. Similar to the argument presented for the lateral shift, this results in 7·10⁻⁵ average variation in rotation rate per single pseudo-projection and does not appreciably affect data collection. For a vast majority of the studied cells (˜95% of cells), the orientation of the rotation axis of the cell remained stable during rotation. Cells that exhibited slight, but appreciable wobbling were excluded from analysis.

In certain embodiments, a hydrodynamic vortex rotation method can be used to rotate the object 220. For this method, a fluid pumping system may be needed. The pumping system may consist of a low (positive) air pressure controller and low vacuum controller. These controllers are upstream of a fluid reservoir filled with deionized water. The reservoir leads to a flow monitoring device that creates a feedback loop for software control of the pressure controllers; it is in this manner that minute fluid flow rates are obtained, with exquisite flow control. The pumping system may incorporate a (cell solution) sample loading loop or other valving downstream of the flow monitor device and upstream of the tomograph device manifold 400.

Referring to FIG. 11 again, following step 1115, in step 1120, the fluorescence emission signal of the object 220 is collected on the camera chip 216 (FIG. 2B) while rapidly scanning objective lens located within the scanning objective 206 along the optical axis 230 over a range that is large enough to interrogate the entire object 220 (FIG. 2B). In certain embodiments, the object 220 is fluorescently labeled by internalizing a plurality of fluorescent beads into the object. Further, in certain embodiments, the scanning objectives 206 can traverse up and down through a continuum of focal planes, by means of, for example, a piezo motion device, collecting data at a continuum of planes through the object 220. The emission signals emanating from the whole object 220 can be integrated by the camera chip during each of such scans effectively producing the mathematical equivalent of a projection that one would obtain with a parallel beam (FIG. 2C). In certain embodiments, a spatial, high-frequency band-pass filter should be used on each projection image to remove the accumulated low-frequency background.

In step 1125, whether a full rotation of the object 220 has occurred need to be determined. In some embodiments, the full rotation comprises a 180 degree rotation. In other embodiments, the full rotation comprises a 360 degree rotation. In yet other embodiments, the full rotation comprises a 720 degree rotation. The specific degrees of the full rotation are not meant to be limiting, in fact, any suitable angular sampling rate between about 0.5 degrees and about 3.0 degrees can be utilized and a corresponding number of projections collected over various rotational ranges, including but not limited to 180 degrees, 360 degrees and 720 degrees. Once a full rotation has occurred, the method transitions to step 1130, wherein a plurality of 2D pseudo-projections are collected. In certain embodiments, about 300 projections are collected over a full rotation at an angular sampling rate of 1.2 degrees. In other embodiments, about 500 projections are collected over a full rotation (360 degrees) at an angular sampling rate of 0.72 degrees. In yet other embodiments, any suitable angular sampling rate between about 0.72 degrees and about 1.2 degrees can be utilized and a corresponding number of projections will be collected over a full rotation (360 degrees). A volumetric (3D) image of the cell is then reconstructed using computational algorithms in step 1135. If a full rotation has not occurred yet, the method transitions to step 1115 and the method will repeat step 1115 and 1120 until a full rotation of the object 220 completes.

When a cell(s) is delivered to the imaging chamber, immobilized and optionally rotated, the objective lens-detector combination may be used to acquire fluorescence (emission), transmitted (absorption), reflected or scattered optical radiation from the object. The collected data is submitted to and processed by computer algorithms to form images, including three-dimensional images, of the object cell. In some embodiments, if the collected data is transmitted or emitted projections of the object 220 at a multiplicity of angles, such data may be submitted for processing by computed tomography algorithms similar to those evoked in radiological X-ray CT. In other embodiments, structural or functional images or other representations of the object 220 can be produced from the acquired, processed data without the use of CT reconstruction algorithms.

This is advantageous for poorly immobilized cells or for cells rotating about an unstable rotation axis. The quantitative images and image analyses automatically produce quantitative biosignatures that can be used, for example, for disease diagnoses and mechanistic studies of biological processes.

An additional functionality of this instrument is its ability to collect the object 220 after optical data (image) acquisition, and deliver the object 220 to a destination container for downstream experiments. The device for this function is shown in FIG. 6. As an example, the device may employ a high precision fluidic pump to collect the object 220 from the tomographic microfluidic device, then deposit the object 220 into a tube (e.g., PCR tube) for the purposes of genomic, transcriptomic, proteomic or other biomolecular analyses. An alternate implementation of end-point analyses is to perform the genomic, transcriptomic, and/or proteomic analyses of the object 220 while they remain inside the tomographic microfluidic device. The microfluidic device already has the ability to introduce other reagents needed for molecular analyses. The microfluidic chip within the microfluidic device can be placed on a thermal plate for any required thermal processing.

While one or more exemplary embodiments of the technology disclosed herein have been illustrated in detail, it should be apparent that modifications and adaptations to those embodiments may occur to one skilled in the art without departing from the scope of the present disclosure.

Methods

Electrocage Microfabrication

In one or more embodiments, the fabrication of the electrorotation chip may require the bonding of two separate chips (180 μm, and 500 μm), with four electrodes each, in order to form an octopole for cell rotation.

Following the fabrication of the individual dies, the final chip for cell rotation is assembled. The 180 μm die with the micropatterned fluidic channel is used as the bottom half of the chip for imaging with high spatial resolution. The two dies are aligned on a mask aligner stage for precision control, and are bonded by wicking in an ultraviolet (UV) adhesive via capillary forces. Once the microchannel has been completely surrounded by the UV adhesive, it is cured under UV-light which creates a permanent seal around the perimeter of the chip.

The electrical connections to the waveform generator are made on a custom PCB board with preexisting contacts. This approach enables convenient handling of the electrocage chip, which sits snugly on this fixture during experiments. There are two fluidic connections. A fluidic probe connected to a 50 μL syringe for precise manual control of fluid in the chip creates a removable, sealed contact with one port of the microfluidic channel on the chip. A pipette tip or nanoport is glued to the other fluidic port and provides an inlet for introducing cells.

Cell Culture and Fluorescent Staining

K562 cells (human chronic myelogenous leukemia cells) were cultured in RPMI 1640 medium (Gibco, Grand Island, N.Y., USA) containing 10% fetal bovine serum (Gibco, Grand Island, N.Y.), 100 units mL-1 penicillin, and 100 μg mL-1 streptomycin at 37° C. in a humidified incubator containing 5% CO2. The cell density was determined using a Countess® II FL Automated Cell Counter (Life Technologies, Grand Island, N.Y.). Cultures were maintained by the addition or replacement of fresh medium. J774A.1 cells (mouse macrophage cells) were cultured in Dulbecco's Modified Eagle's Medium supplemented with 10% fetal bovine serum (Gibco, Grand Island, N.Y., USA), 100 units mL-1 penicillin, and 100 μg mL-1 streptomycin at 37° C. in a humidified incubator containing 5% CO2. Subcultures were prepared by scraping a 75 cm² flask, removing all but 10 mL of the culture medium. The volume was adjusted accordingly for different culture vessels. The cell density was determined using the automated cell counter. The nucleus and mitochondria of K562 and J774A.1 cells were stained simultaneously using Hoechst 33342 (H3570, Life Technologies, Grand Island, N.Y.) and MitoTracker Red (M-7512, Life Technologies). For staining, 3 μL 0.5 μM Hoechst 33342 and 3 μL 1 μM MitoTracker Red into a 1 mL 1 million cell sample was added and incubated for 30 minutes.

Live Cells with Beads Samples

To characterize spatial resolution of the system, 200 nm diameter beads (Life Technologies) were combined with live K562 cells and imaged the cells with LCCT. To make the live cell and beads sample, 1 mL of cell medium containing 10⁶ K562 cells were put into a PCR tube and mixed with 0.3 μL of bead stock. The sample was incubated for 4 hours to make sure the beads were taken up by the cells. After 4 hours, the sample was centrifuged at 500 g for 7 minutes to form a pellet, the supernatant was aspirated and pellet re-suspended in 1 mL of fresh cell culture media. The centrifugation step was repeated twice to remove any remaining free beads in the media.

Effects of Electrocage Parameters on Cell Rotation Rate

The magnitude of the dielectrophoretic (DEP) force is proportional to the gradient of the electric field, which is generated by the voltage applied to the microelectrodes. Therefore, an appropriate starting voltage should be chosen for experiments. The experiments were set up with a frequency fixed at 2 MHz, and were done on a chip with a 50 μm spacing between the two electrode layers. The diameter of the tested human myelogenous leukemia cells (K562 cell line) was 18±3 μm. A graph of the number of K562 cells successfully rotated as a function of voltage applied is shown. Based on this result the amplitude value of 2 V (peak-to-peak) were chosen as a good starting point to perform electrorotation.

It is known that the frequency of the electric field is closely related to rotational parameters like speed, and rotational direction. In order to determine the relation between different frequencies and the cell rotation rate, the voltage amplitude was fixed at 2 V while the frequency was adjusted from 0.2 to 7 MHz. The cell size, and chip electrode gap were the same as for the previous voltage amplitude study. As expected, an increase was observed in the rotation rate with increasing electric field frequency. A slow rotation rate, of 1-2 RPM is desirable for collecting a large number of pseudo-projections during one full rotation of the cell. The number of collected pseudo-projection images directly affects spatial resolution and SNR of the reconstructed volumetric images. Therefore, a working frequency range of 0.5-2 MHz was chosen as best suited for imaging. Lower frequencies were avoided as they may induce ionic currents leading to cell stress and membrane electroporation, while higher frequencies were observed can lead to cell membrane damage.

Rotational Stability Characterization

Two types of distortions of an ideal rotation were observed in the electrocage that can affect the quality of 3D image reconstruction from 2D pseudo-projection images: 1. Lateral shift in the XY plane of the cell, and 2. Alterations in rotation rate. The average lateral shifts were determined along the X and Y axes during a full rotation of the cell to be 0.057 and 0.084 in units of cell diameter. Assuming a 10 μm diameter of a typical cell, the average lateral shifts are 0.57 μm and 0.84 μm per full rotation of the cell. Our typical rotation rates are in the range of 1-3 rotations per minute (RPM) or 20-60 seconds/rotation. The average lateral shift per each individual pseudo-projection were calculated as follows:

$s_{ave} = {S_{ave} \times \frac{t_{{ex}\; p}}{T_{rot}}}$ where S_(ave) is the average lateral shift along one axis per full rotation, t_(exp) is the exposure time for individual pseudo-projection image acquisition, and T_(rot) is the rotation period. Assuming a 30 second rotation period and given the average lateral shifts along the X and Y axis of 0.57 μm and 0.84 μm, respectively, and the exposure time of 40 ms, the s_(ave) values of 0.76 nm and 1.12 nm for X and Y axis were calculated respectively.

Our cell stability measurement yielded an average rotation rate instability of 5.1% per full rotation. Assuming a typical rotation rate of 2 RPM (30 second rotation period), this translates into an absolute average instability of 0.1 RPM per full rotation. By analogy with the lateral shift calculation consideration, the average rotational rate error per individual pseudo-projection was defined using the following equation:

$r_{ave} = {R_{ave} \times \frac{t_{{ex}\; p}}{T_{rot}}}$ where R_(ave) is the average deviation of the rotation rate in RPM, t_(exp) is the exposure time for individual pseudo-projection image acquisition, and T_(rot) is the rotation period. Given t_(exp)=0.04 s, R_(ave)=0.1 RPM, and assuming a T_(rot)=30 s, a rotation rate instability per pseudo-projection r_(ave)=0.00013 RPM was calculated.

Both types of instabilities—lateral shift and rotation rate instability—are small and do not affect the image quality of pseudo-projections appreciably in terms of introducing movement artifacts.

In addition to the deviations in lateral position and rotation rate of the cell, the fact that the cell rotates continuously during pseudo-projection acquisition should be considered. Consequently, one needs to determine the amount of blurring that will occur due to the cell movement and how it may affect image quality. To this end the upper limit of such distortion was determined by calculating the distance that a pixel at the outer boundary of the cell would travel during the exposure time over which a pseudo-projection image is acquired. Using the same assumptions as above, i.e. a 10 μm cell diameter, 30 second rotation period (360 degree rotation), the angular distance of the cell during the 0.04 s exposure time for image acquisition was calculated as:

$\alpha = {{360 \times \frac{t_{{ex}\; p}}{T_{rot}}} = {{360 \times \frac{0.04}{30}} = {0.48\mspace{20mu}{degrees}}}}$ The distance a pixel at the outer boundary of a 10 μm diameter cell would travel is the arc length:

$l = {{2\;\pi\; r\;\frac{\alpha}{360}} = {{6.28 \times 5 \times \frac{0.48}{360}} = {{0.042\mspace{14mu}{µm}} = {42{\mspace{11mu}\;}n\; m}}}}$ where r is the cell radius. The motion blur is small compared with the spatial resolution of the system (280-290 nm at 550 nm) and can be neglected. Assessment of Cell Stress in High Frequency Electrical Fields

To assess potentially adverse effects of exposure to a high frequency electric field on cell health a series of experiment were performed to assess cell stress. It is possible that external electric fields generated by the electrocage may induce a stress response that manifest in changes in the biomolecular profile and/or alterations in morphology of the cell. To address this question, Applicants first designed and implemented a test platform to evaluate the stress at the bulk sample level. Applicants used the well-understood electrical properties of a set of parallel electrodes that produce a uniform electric field E=V/d, where V is the voltage difference between the electrodes, and d is the separation between them. When the size of the electrodes is significantly larger than d, the electrical field leakage at the edges is negligible. The device is composed of two 2×3″ glass microscope slides with gold coating on one side, and a 50-μm-thick spacer made of adhesive non-conducting tape used in the semiconductor industry. The separation between the electrodes is the same as that in the electrocage. After cells suspended in growth medium are pipetted onto one of the electrodes, the medium is sandwiched with a second electrode and a voltage of 2 V at 2 MHz is applied across the electrodes. The same alternating current waveform was selected as that used in the electrocage, so the electrical stress between the plate electrodes can effectively simulate the actual situation during the cell rotation process in the electrocage. The presence of cellular stress was tested under five different conditions followed by western blot analysis to monitor expression levels of the heat-shock protein 70 (HSP70). HSP70 protein was chosen as stress indicator owing to its central role in the cellular stress response and high sensitivity to a broad variety of stress types. For each stress condition, approximately 2 million cells were loaded into the uniform electrical field. A total of five conditions were tested: Condition 1: Control. Cells were kept in the incubator with no disturbance. Condition 2: Cells sandwiched between electrodes with no electrical field applied. This condition was tested to ensure there is no significant stress caused by transferring cells onto the device. Condition 3: Cells between electrodes with the electrical field applied. Condition 4: Same as in Condition 3 followed by a recovery phase at 37° C. for 4 hours. Condition 5: Conventional heat shock at 42° C. for 10 minutes as a positive control.

No significant increase was found in HSP70 expression levels resulting from the exposure to the electric field. In addition to the HSP70 expression levels, time-lapse imaging of live K562 cells was performed where Applicants investigated whether a prolonged continuous exposure (>4 minutes) to electric field would induce measurable changes in cellular morphology. In line with the HSP70 findings, the imaging revealed no gross changes in cell morphology (cell size and overall intracellular organization) at sub-micron resolution during a continuous 4-5 minute rotation in the electrocage. Furthermore, turning the electric field on or off in the electrocage did not cause measurable changes in cellular morphology (data not shown). These findings suggest that the electric fields used for cell rotation in the electrocage do not cause measurable stress levels in live cells under experimental conditions used for imaging.

System Setup

One of our main goals was to design a system simple enough to be replicated and implemented in any research or clinical lab. Therefore, the system was designed around a standard, commercially available inverted microscope with epi fluorescence imaging capability. A schematic of the LCCT system, and a picture of the complete system setup is shown in FIG. 1. Except for the microfluidic chip for cell rotation, all other parts are commercially available with some of them, such as the LED light source and CCD camera, already in routine use by many labs worldwide. The system comprises three main sub-systems: 1) cell rotation system; 2) epi fluorescence image acquisition system; 3) optional confocal optical imaging system for comparison purposes.

The cell rotation sub-system is composed of an integrated microfluidic chip (“electrocage”) that combines a microfluidic channel and 8 microelectrodes arranged on two layers. An 8-channel computer-controlled waveform generator was used to create sinusoidal waveforms to drive the electrocage.

The image acquisition sub-system comprises an inverted microscope equipped with appropriate filters for fluorescence detection and a LED light source connected to the back port of the microscope for fluorescence excitation. A fast-scan piezo stage with an objective lens mounted on it is used to sweep the imaging plane of the objective lens through the entire cell for obtaining projection images. A dual-channel spectral module (DCSM) is attached to the right port of the microscope to split the emission photons coming from two different fluorophores into respective halves of the detector chip. Fluorescence is detected by a cooled electron-multiplying charge-coupled device (EMCCD) camera. Custom software was developed to control timing and synchronization between the cell rotation and imaging modules.

For comparison with and validation against another, already established imaging modality, a commercial confocal swept-field module was integrated onto the left port of the microscope. This capability is optional for routine CT imaging.

The system setup is built around a commercial inverted microscope (Ti-U, Nikon, Melville, N.Y.) equipped with a Xenon arc lamp (wavelength range 320 nm to 700 nm, Lambda LS, Sutter Instruments) or a multi-LED light source connected to the back port of the microscope for fluorescence excitation. A filter cube mounted in the microscope turret contains an excitation filter (Semrock, FF01-407/494/576-25) and a multichroic beamsplitter (Semrock, FF436/514/604-Di01-25×36) for selecting appropriate excitation wavelengths that are absorbed by the fluorophore and blocks other sources of light. A fast-scan piezo stage (part of the confocal swept-field module described below) with an objective lens (S Fluor, 100× oil, NA 0.5-1.3, Nikon) mounted on it is used to scan the image plane of the objective lens. An intermediate 1.5× lens mounted inside of the microscope was used to obtain a total optical magnification of 150× (100× objective lens×1.5× intermediate lens). A dual-channel spectral module (DCSM) (DV2, Photometrics, Tucson, Ariz.) is placed on the right port of the microscope to spectrally split the emission photons coming from two different fluorophores into respective halves of the detector chip. Each spectral channel is projected onto one half of a camera chip simultaneously using two emission filters (FF01-452/45-25, Semrock and ET630/75m, Chroma Technology) mounted in the DCSM. A cooled EMCCD camera (Evolve 512, Photometrics), is connected to the DCSM and is used to acquire projection images. The physical pixel size of the camera is 16 μm×16 μm resulting in an optical pixel size of 16 μm/150× magnification=0.107 μm (107 nm). The optical pixel size is below the Nyquist criterion for diffraction-limited resolution of 258 nm/2=179 nm (assuming 550 nm emission wavelength and numerical aperture of 1.3) and thus satisfies the spatial sampling requirement. An optional swept-field confocal module (SFC) (Prairie SFC, Bruker, Billerica, Mass.) is attached onto the left port of the microscope for comparison purposes. The SFC is equipped with four lasers with excitation wavelengths of 406, 488, 532, and 561 nm. The confocal image acquisition was accomplished using PrairieView v.5 software.

In certain embodiments, an example of the hardware's integration and synchronization software, a custom LabVIEW (v. 2015, National Instruments, Houston, Tex.) program was written to trigger and synchronize the piezo stage with the camera. This software ensures that all required imaging and acquisition tasks are carried out in the proper sequence, and with the proper timing and synchronization. Further, this software contains a suite of custom designed independent software modules that are integrated with each other via software and electronic messaging. The functionality of the software includes, but is not limited to, precise timing and triggering of system image acquisition hardware (e.g., camera exposure), precise timing and triggering of system fluorescence illumination (e.g., LED control), precise timing and synchronization of cell rotation hardware (e.g., electrocage), configuration and control of amplitude, frequency, and phase for the electrocage octopole, precise timing and synchronization of piezo actuated objective (e.g., the ‘piezo controller’), and control and automation of ancillary imaging hardware (e.g., stage controllers).

It is important to have proper timing and synchronization of the imaging acquisition hardware with the piezo controller. In order to gather proper projection data, the start and stop exposure time of the image acquisition hardware must be precisely synchronized with the piezo controller, to ensure that optical data is only collected at the proper time during the sweep of the objective through the imaging volume that encompasses the cell of interest. If projection data acquisition is not initiated and terminated at the optimal time during the sweep of the objective lens, then data will either be lost, or data will be acquired during a non-linear portion of the objective's sweep, causing distortion. In either case, degradation in the 3D image reconstruction will result. In some embodiments, in order to achieve the desired image fidelity, with the stated temporal resolution and sample rate, the tolerance for error during this part of the hardware/software synchronization process is on the order of microseconds.

EXAMPLES Example 1 Imaging Human Myelogenous Leukemia Cells

After establishing reproducible and stable cell rotation, a series of imaging experiments were performed with live K562 cells with the goal of characterizing the spatial and temporal resolution of the method. To determine spatial resolution, fluorescent 200 nm diameter beads internalized through endocytosis into cells as a “biological” phantom were used. The beads were incubated with cells at low concentration so that each cell contained on average between 3-10 beads distributed sparsely inside the cell. The cells were then rotated and imaged in the electrocage under the same conditions as in a typical experiment. Reconstructed volumetric image data were used to characterize the point spread function (PSF) of the system (FIG. 7D). The spatial resolution was determined as the full width at half maximum (FWHM) of the bead intensity profiles (FIG. 7E, F). A resolution was found in the planar (XY) direction of 311±20 nm and 280±34 nm in the axial (XZ) direction. This corresponds closely with the theoretical limit of the spatial resolution due to diffraction of 258 nm and expressed as d=0.61λ/NA, where λ is the wavelength of the emitted light (550 nm in our case), and NA is the numerical aperture of the objective lens (1.3 in our experiments). The optical pixel size of the system is 107 nm, which satisfies the Nyquist sampling criterion of 258 nm/2=179 nm for diffraction-limited spatial resolution (Online Materials and Methods). Despite the about 10% difference in the spatial resolution along the two directions, the difference is within the measurement error.

Our approach of using point-like emission sources such as beads inside of live cells closely replicates real imaging conditions. This strategy accounts for refraction and scattering inside the cell in contrast to the widely used method of using fluorescent beads suspended in pure aqueous solution as a calibration standard. One of the factors determining spatial resolution of our approach is the number of projection images collected during one full rotation. The higher the number of projections the denser is the spatial sampling of the object, which translates into a more accurate representation of the spatial object's data.

There are several limiting factors determining the speed at which projection images can be collected. First, the number of fluorescence photons emitted by the fluorophore molecules over a unit of time (emission intensity) and consequently the signal-to-noise ratio (SNR) of the image play a major role. One can increase the emission photon intensity by increasing excitation light intensity at the expense of increased photobleaching rate. As a result, a balance between SNR and photobleaching needs to be established. Applicants could reliably acquire projection images with an SNR of 4-10, when collecting images at a rate of 10 frames per second without causing significant photobleaching for continuous imaging over 5 minutes using the Hoechst 33343 nuclear stain. Second, in our approach the number of projections that could be collected during one full rotation was the scan speed of the objective lens (FIG. 2B). Due to considerable weight of high numerical aperture objective lenses (˜300 grams), inertia becomes a limiting factor by distorting the linear part of the scan at speeds higher than 10 scans per second.

The temporal resolution of the approach was determined as the time needed to collect a full set (300-400) of projection images with high SNR to produce high quality volumetric reconstructions. To this end, Applicants stained the nucleus and the mitochondria of K562 cells and collected projection images at rotation speeds of 1-3 RPM, which resulted in a temporal resolution of 60-20 seconds/volume, correspondingly. We note that we were able to achieve higher temporal resolution of down to ˜7 seconds/volume by increasing the rotation speed and reducing the number of projections per rotation. However, the overall quality of the reconstructed volumes was reduced resulting in lower SNR.

For characterization purposes, Applicants compared 3D imaging capabilities of the LCCT approach with those of confocal imaging, which is widely used in the field of biology and biomedical research. To this end, the same cells were imaged with fluorescently labeled nuclear DNA using both imaging modalities, where Applicants reconstructed 3D images of the nucleus from confocal Z-stacks or pseudo-projection data in case of LCCT (FIG. 7G). As expected, Applicants found that the typical elongation of the object along the optical axis of the system due to inferior spatial resolution in confocal imaging was absent in 3D reconstructed images obtained with LCCT due to isotropic spatial resolution. This illustrates the advantage of the LCCT approach to perform orientation-independent measurements of live cells with the same spatial resolution along all three spatial directions.

A representative example of reconstructed volumetric data of a live K562 cell with fluorescently stained nucleus and mitochondria is shown in FIG. 8. The nuclear stain revealed a highly varying pattern of DNA density distribution inside the nucleus presented as maximum intensity projection (MIP) images taken at three orthogonal orientations (FIG. 8A). Furthermore, the volume rendering image (false-colored panel) shows surface textural features and a fold-like feature (FIG. 8A, arrow) with co-localized mitochondria (FIG. 8C, D). Overall, the mitochondria of the cell appear distributed in a non-random manner around the nucleus.

Feature extraction was performed based on the intensity histogram thresholding to demonstrate the ability to obtain quantitative information about nuclear and mitochondrial structural features. FIGS. 8E and F depict segmentations of the nuclear and mitochondrial features (colored overlays on MIP images) in the upper 30% of the intensity scale, correspondingly. The rather simple segmentation illustrates how quantitative 3D spatial information (center of mass coordinates, volumes, relative positions etc.) about structural features can be extracted and analyzed. While MIP images represent a good solution for presenting 3D data in 2D format, their intrinsic limitation lies in that a substantial amount of intensity detail is lost, especially when presenting densely distributed small structures with varying intensity. To illustrate the appearance of mitochondria more adequately, Applicants show these organelles in form of slices at 5 different depths of the reconstructed 3D image along with the corresponding segmentation results (FIG. 8G).

It is possible that the mitochondrial dynamics during the collection of a full set of projection images (full cell rotation or 30 seconds) may lead to a blurred appearance of the mitochondria in volumetric images. To evaluate this aspect, mitochondria was imaged in fixed K562 cells using confocal microscopy. In both cases, the mitochondrial morphology is qualitatively similar and appears as complex, dense 3D networks distributed around the nucleus.

Example 2 Nuclear and Mitochondrial Dynamics Study

Mitochondrial Fission Inhibition

To validate our approach Applicants conducted a series of experiments where Applicants compared mitochondrial distribution characteristics in untreated naïve mouse macrophages (cell line J774A.1) with macrophages treated with an inhibitor of mitochondrial dynamics. Applicants expected to observe an increase in the mitochondrial volume resulting from treatment with validated mitochondrial division (fission) inhibitors. Applicants compared two different inhibitors, mdivi-1 and 8-bromo-cyclic adenosine-mono-phosphate (8-bromo-cAMP), as they were demonstrated to show large effects on mitochondrial dynamics. After conducting preliminary confocal imaging, 8-bromo-cAMP was found to exhibit the largest effect on mitochondria distribution. Applicants collected image data and performed mitochondrial morphology analysis of 30 untreated J774A.1 cells and 30 cells after treating them with 8-bromo-cAMP. A representative comparison of 3D reconstructed volumetric images is shown in FIG. 9A. Because of isotropic spatial resolution offered by the method, the absolute volumes of mitochondrial clusters were calculated.

Applicants developed a custom computational pipeline for processing reconstructed 3D images. The pipeline includes the background subtraction, contrast enhancement through intensity normalization steps, followed by adaptive local thresholding using the Niblack method, and a 3D object counting steps based on the nearest neighborhood connectivity. As expected, marked alterations were observed in the mitochondrial distribution, namely in a fraction of cells formation of large clusters as compared with seemingly smaller, more diffusely distributed mitochondrial structures in untreated cells (FIG. 9B, C). Interestingly, not all cells exhibited such dramatic changes in mitochondrial distribution. After a close examination of all recorded datasets it became clear that a portion of the treated cells exhibited less marked changes in the mitochondrial volume. A detailed analysis of the entire sample revealed a less pronounced, but statistically significant increase in the average volume of mitochondrial structures after treatment (FIG. 9D, E).

While our developed image processing and feature extraction pipeline gave satisfactory results, it also can be improved to better account for the interconnectivity between the mitochondria. Custom computational tools and optimized pipelines need to be developed to account for specific aspects of features such as joining, intensity variation, local noise patterns, etc. Improved feature extraction pipelines specifically tailored towards 3D volumetric imagery of cells in suspension would benefit full utilization of the quantitative aspect of the approach.

Mitochondrial and Nuclear Dynamics Studies

A series of experiments were conducted in which Applicants used the LCCT platform to explore intrinsic temporal characteristics of nuclear and mitochondrial morphology in mouse macrophages (J774A.1 cell line). For each cell Applicants acquired projection images over a time period of ˜3-5 minutes during continuous rotation a temporal resolution of 30 seconds. Applicants acquired nuclear and mitochondrial morphology data for a total of 30 J774A.1 cells. The data analysis of the reconstructed 3D images was performed using a similar analysis pipeline as outlined in the mitochondrial fission inhibition study above. Briefly, the images were background subtracted and a local adaptive threshold was applied. After intensity normalization features were extracted using a 3D object counting routine based on nearest neighborhood connectivity. Data shows a representative time-lapse 3D reconstruction of a J774A.1 cell. Applicants extracted and calculated features for both mitochondria and the nucleus. Due to isotropic spatial resolution provided by the LCCT method, Applicants could determine a number of quantitative morphology characteristics, including feature volumes, positions of the centroids and center of mass with respect to the 3D volume, and mean densities. FIG. 10 shows a typical dataset of feature volume distribution over time for the cell. In general, features in the lower range of volumes (0.01-0.1 fL) were observed to show markedly less temporal variation in volume as compared to the larger structures (0.1-0.3 fL).

Example 3 Image Processing and Reconstruction

The current method allows precisely aligning optical or other projections acquired with electromagnetic radiation (visible light, for example) from an object. Raw data for the Cell-CT is collected by scanning the focal plane of a standard light microscope through the cell at each of hundreds of rotational angles, integrating all the collected light on an open-shuttered CCD chip. Since the resulting images at each angle are similar, but not identical, to parallel transmitted pencil-beam projections, they were called “pseudoprojections”. Three challenges must be addressed in any attempt to create a high quality reconstruction: First, the background of each projection must be removed. Second, the projections have to be aligned with submicron precision. This can be challenging because cell motion during data acquisition cannot be perfectly characterized. Last, the effects of artifacts common to bright field microscopy such as uneven lighting and debris in the field of view must be minimized.

Data Acquisition

Projections are formed as illustrated in FIG. 6B. The sample cell-gel suspension is injected into a rotating capillary (imaging chamber) mounted in a special cartridge on the stage of the bright field microscope. Everything between the condenser and the objective lens is optical index matched to prevent distortions arising from the curved capillary wall. In certain embodiments, the capillary is rotated at a much slower rate than the focal scan frequency. To first order approximation, the resulting images are the same as standard pencil beam projections.

Some deviations from ideality are discussed in R. L. Coe and E. J. Seibel, “Computational modeling of optical projection tomographic microscopy using the finite difference time domain method,” Journal of the Optical Society of America A, vol. 29, pp. 2696-2707, 2012, which is incorporated herein in its entirety.

Background Correction

The use of standard illumination correction techniques to remove the background of these images is complicated by the scanned focal plane mode of data acquisition. In addition to the factors that normally contribute to imperfect projections, such as uneven illumination, pixel sensitivity, and stationary dust in the optical path, additional sources of imperfection in cell CT include the slight, angle-dependent refraction index mismatch of the capillary, debris that rotates into the cell path, and illumination intensity fluctuations over the course of data acquisition. The capillary lighting and the debris pose the most difficult challenges. Because the precessing cells are tracked, and the focal plane (FIG. 2C) scans up and down as the capillary rotates, a different chord segment of the capillary is sampled at each angle. This results in each pseudoprojection having its own background illumination. Though, as shown in FIGS. 12 and 13, deviation from the mean background is strongest near the edges of the capillary, the intensity distribution at the center of the capillary is also affected.

The standard method in bright field microscopy for dealing with uneven illumination is to acquire a series of images with no sample in the microscope. These images are averaged to produce a smooth image unaffected by read noise. A background produced in this way is termed the flood field.

Because of the inadequacy of the flood field method for background removal from cell CT pseudoprojections (PPs), Applicants developed a method, schematized in FIG. 13, to remove the contribution of the capillary from each projection. This was performed by masking the location of the cells using the Otsu threshold as described in N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems, Man and Cybernetics, vol. 9, pp. 62-66, 1979, which is incorporated herein in its entirety. After the removal of absorbing objects, the intensity profile of the capillary was determined by forward projecting each projection along the capillary axis direction, forming the profiles depicted at the top right of FIG. 13, then using an accelerated trimmed least squares curve fitting to estimate the contribution of the capillary to each row in the projection. Applicants smoothed the resulting profile to prevent this step from introducing high frequency noise into the image. This background intensity is then subtracted from the pseudoprojection. The capillary intensity images are in general background intensity images.

After the individual PP capillary corrections are performed, the more uniform background is created using a masked average of all the PPs as a starting point. This averaging produces its own artifacts; the most noticeable, seen in the background of FIG. 13, being ghost traces at locations the cell occupies during its rotation.

The background is refined by using the principles of total variation noise reduction (TV). Assuming a multiplicative image noise model, as described in Aubert and Aujol, the functional takes the form described in G. Aubert and J.-F. Aujol, “A Variational Approach to Removing Multiplicative Noise,” SIAM Journal on Applied Mathematics, vol. 68, pp. 925-946, 2008, which is incorporated herein in its entirety: E(x)=∫Dx+∫(log x+y/x)=∫Dx+∫(log x+b)  (2) where ∫Dx is the variation of the signal. For image denoising the function that must be minimized becomes F=min_(x>0)Σ_(N)Σ_(S)(log|x _(n) |+y _(n/) x _(n))+α∥x _(n)∥_(TV)  (3) Where N is the number of representative images taken from the whole list of PPs, S is the vector space, α is the balance factor that determines how much smoothness is required, and ∥x_(n)∥TV is the variation in x. The ratio y_(n)/x_(n) is initialized to the background b. After each round of iterations for the minimization of this functional for x_(n), the background is regularized to the average of all the x_(n). This technique allows the ensemble background to be minimized against a number of backgrounds, while preventing debris or local gel variations from disrupting the final result of the background. It also allows a significant speed increase over performing TV on each individual pseudoprojection.

Two advanced filtering methods were tested to determine how well the PPs could be corrected without performing TV after subtracting the capillary intensity profile. These methods are the SAFIR filter and block matching filtering, BM3D. These methods were applied to every PP in a data set. Both are far more computationally intensive than TV. For SAFIR filter, please see Kervrann C, Boulanger J. 2006. Optimal spatial adaptation for patch-based image denoising. IEEE Transactions on Image Processing 15:2866-2878. doi: 10.1109/TIP.2006.877529.

The quality of the background correction achieved by the various methods was determined by analyzing the pixel variation in a patch near the cell which was never occupied by the cell or debris in any projection. A measure of the camera read noise was determined by taking the variation of a single pixel over the series of all pseudoprojections in the acquisition. This comparison is shown in FIG. 14(G) and the TV refined method produces the least intensity variance.

Marker Free Registration

Alignment of the projection data is another important step for creating a quality reconstruction. It is common to have multiple cells in the same field of view, but only one can be followed through the rotation. Cells can wander over extensive and unpredictable paths during data acquisition. There are no useable fiducial marks, so the registration and alignment must be performed with only the information available in the projections themselves. The desired cell is indicated by the user before data acquisition starts, and its motion is followed through all the projections by judicial use of center of gravity and watershed algorithms to mask the debris and non-target cells. This procedure results in a crude alignment of the cell projections, but there are jitter and misalignments that occur from noise and irregularly shaped cells.

This alignment procedure is accurate to within a few pixels. In order to produce very good reconstructions, the alignment must be further refined. Applicants applied center of gravity (COG), noised center of gravity (nCOG), masked center of gravity (mCOG), cross correlation (CC), and template matched correlation (tCC) to achieve this fine-tuned alignment. CC and COG are well known, commonly used techniques. The nCOG method adds coherent (Perlin) noise followed by COG computation multiple times on each pseudoprojection.

Tuning the noise amplitude to the level of the systematic noise helps to prevent the acquisition noise and gel irregularities from causing jitter. mCOG performs an adaptive thresholding operation to produce a mask of the already found cell and then a feathering operation is performed on the mask to limit the effect of noise and debris on the final registration before aligning the cell with COG. tCC is performed by taking the roughly aligned PPs, and performing a low quality reconstruction of the data. The forward projections from this crude reconstruction are then used as templates to do the final alignment of the PPs by cross correlation.

In the absence of a useful phantom, evaluation of the quality of the registration must be taken from the data itself. The quality of the registration techniques is evaluated following the method of Cheddad whereby the registered image is correlated with its mirror image taken at the opposite angle. The results in FIG. 15 show that CC and nCOG perform better than the other methods. nCOG is much faster than CC due to its simplicity. The technique helps to remove the deleterious effects of stray noise, debris and illumination.

Volumetric Reconstruction of Projection Data

Reconstructions of LCCT projection data are computationally simpler than most tomographic reconstruction methods. The LCCT projection can be best approximated as a series of pencil (parallel) beams, as opposed to fan beams in conventional CT, extending from each camera pixel and rotating in a cylinder around the rotation axis. This geometry lends itself to a few high performance algorithms.

Based on these considerations, a highly optimized algorithm has been developed to markedly reduce the computational time required for reconstructions. The speed-up is achieved by structuring the code based on rays extending from the voxels in the reconstruction volume, instead of the conventional method of working from rays extending from the projection. This change in the perspective of the algorithm greatly reduces the number of calculations that must be done to determine which voxel is affected by each ray. The second greatest cost for the reconstruction algorithm is in the interpolation of the ray's path through a voxel, and thereby determining the weighting of the rays' effect on that voxel. As these calculations can be computationally expensive, the interpolations were performed once for just one row of the whole volume. Once the weighting set was calculated, the weights could be used for all the rows in the pseudo projections. This means that the interpolations are only performed once per projection instead of once per voxel. This computation can reduce the time of the calculation by 2-3 times. Lastly, the code has been optimized to run within OpenCL allowing the code to be run on either a graphical processing unit or in a multiprocessor environment.

A variety of reconstruction methods were tested to determine which could produce the best result in terms of reconstruction quality and speed. These techniques include filtered back projection (FBP), simultaneous iterative reconstruction technique (SIRT), Markov random field regularized reconstruction (MRF-RR), Tikhonov regularized iterative reconstruction, (TV regularized SIRT) and online blind deconvolution reconstruction. Each method was tested against a number of standards. The advantage of the optical system is that the cell can be optically sectioned, giving a built-in phantom for the reconstruction. The optical section is fairly low quality, so care must be taken in the comparison of the reconstruction and the volume. A few metrics have been developed and tested against human intuition to evaluate the quality of the reconstructions. Finally, all the reconstructions have been evaluated against a simulated cell phantom.

Applicants performed the parallel beam FBP using the techniques outlined in 42. The projections were conditioned as described above by first subtracting the microscope background and correcting the overall intensity. Finally, Applicants reconstructed the 3D volume using a Hann filter and bilinear interpolation. Blind deconvolution of the projections offers a number of advantages for iterative reconstruction methods. Within the framework of SIRT, instead of first completely deconvolving all the pseudoprojections and then backprojecting, the forward and backprojection processes become part of each deconvolution iteration, allowing the determination of successively improved estimates,

x′

_pp, leveraging information available from the projection set as a whole. The breakdown of the technique is as follows:

For each projection angle over 180 degrees:

-   1. Estimate initial x_pp{circumflex over ( )}′ and f_pp{circumflex     over ( )}′ by using the acquired projection (ypp) and its mirror     twin (180-degree pair of parallel projections). -   2. Perform SIRT to create a crude reconstruction using all acquired     projections (current 3D image estimate V′). -   3. Forward project V′ to get x_pp{circumflex over ( )}′ and use this     image to provide the current estimate for the deconvolution. Refine     f_pp{circumflex over ( )}′ and x′pp using the regularization     technique (Markov random field, Tikhonov, total variation, or blind     deconvolution) for several iterations using the ypp and its mirror     (180o) twin. The additional benefit of this operation is that     x_pp{circumflex over ( )}′ alignment is refined on each iteration. -   4. Backproject the difference (error function) between the current     forward projection and x′pp. -   5. Repeat 3-4 until a stop condition is reached.

In certain embodiments, referring to FIG. 17, a rough estimate of the position is made by using the center of gravity and then smoothing of the traces. The registration is further improved by dividing the intensity background from the image to remove the pixel variations from the image. A random jitter filter (Perlin noise) is applied to the pseudo-projection to help wash away the existing pixel variation. This slightly noised image is then blurred and then the center of gravity method is once again applied to determine the position of the cell. This procedure is repeated a number of iterations and the positions are averaged together to give the noise resistant center of the cell. Finally, the x and y centers from all the pseudo-projections are smoothed by fitting the data to the appropriate curve.

Once again the center of gravity method is used to provide a rough estimate of the centers of the cell projections and the size of the cells in all the pseudo-projections and these trace estimates are smoothed. The cells are then cut from the pseudo-projections, and can be registered by phase correlation with a model of the data. In the first approximation, the cell can be modeled by a sphere or a delta function. A second approximation is to register the image with a more detailed generic model of the cell. An egg phantom works for this.

Referring to FIG. 18, a filtered back projection is used with a minimal number (˜25-40) of pseudo projections that have been reduced in size. Then a limited number of forward maximum intensity projections (MIP), or just simple forward projections, are created from this crude reconstruction estimate. An alternative is to produce all the forward projections. These forward projections are the average of all the input projections and provide a very stable standard for the phase correlation method of registration.

In order to provide the best target image to the registration routine, without having to produce an onerous number of MIP projections, the limited number of MIPs are interpolated for comparison to each pseudo-projection. Once the centers of gravity of all the cells' projections have been determined, the cells are correctly cut from the pseudo projections and the full reconstruction can begin without any need for further smoothing of the traces.

This technique allows the fine alignment of computed tomographic (CT) projections by use of a variety of advanced registration methods, which provides improved detail and contrast in the 3D reconstructions.

Data Correction and Image Reconstruction Using Sub-Resolution Fluorescent Beads as Fiducials

For spatial resolution characterization of the LCCT system Applicants used cells containing 3-10 internalized 200 nm diameter fluorescent polystyrene beads. The bead size was chosen so that one can distinguish between single beads and clusters of two or more beads. Owing to the point source-like fluorescence intensity distribution of the beads, as opposed to more diffuse signal when using fluorescent markers inside the cell, the image processing and reconstruction algorithms outlined in the previous section need to be modified for achieving quality reconstructions. Namely, the registration algorithms are based on the alignment of center of mass of more or less diffusely distributed fluorescence signal inside the cell. In case of sparsely distributed few bright spots representing beads, the center of mass approach needs to be modified to perform reliable registration which results in distorted volumetric reconstructions. Applicants have developed another image processing and reconstruction computational pipeline that is not based on center of mass registration and accounts for the specifics of fluorescent beads signal. Also, rotation of cells containing the dielectric polystyrene beads was associated with more pronounced lateral shift along the axis of rotation as compared with cells that did not have beads inside. It is likely that the dielectric properties of the beads cause local distortions of the electric field leading to the observed net translation that needs to be corrected computationally.

To precondition the raw projection data for the image reconstruction algorithm, the first step in the image processing is to characterize and correct for non-uniform rotation and rotation axis. Simulations were performed using Matlab (v. R2016a, MathWorks, Inc., Natick, Mass.) to assess the effects of a rotation axis translation and in-plane rotation, as well as the more complex case of out-of-plane axis rotations.

In conventional CT, projection data is often sorted into a “sinogram” format where data acquired with a 1D array of sensors is presented as a function of viewing angle in a 2D image. This ordering of the data shows the change in attenuation for each detector as a function of viewing angle. In the case of LCCT, Applicants consider the direction within a projection that is parallel to the axis of rotation to be the “detector” direction, and the perpendicular direction to be the “slice” (analogous to multi-slice CT) direction.

A robust, automated algorithm was developed to detect in-plane perturbations using data acquired with beads. The algorithm is based on first tracking the detector sinogram of the various beads, then using the deviations from the fit to an ideal sine curve to derive a set of measured and ideal locations for each projection. This information is used as input to a rigid body transformation estimate. Finally, this estimate is used to correct each projection with rotation and translations. The algorithm is called “geoFit”. GeoFit was applied to a data set of a cell containing >3 beads. The sinograms in show uncorrected vs. corrected results demonstrating a marked improvement of the reconstructed volumetric images of the beads in the cell.

So-called “out-of-plane” errors can be thought of as a change in orientation of the rotation axis with respect to the imaging plane. In contrast to the in-plane errors, which act upon individual projections and can be corrected on a projection-by-projection basis, “out-of-plane” rotations affect multiple projections and must be derived from a consideration of the 3-dimensional object as a whole. The algorithm to characterize and correct such deviations relies on a comparison between measured and computed projections. The computed projections are derived from a 3-D reconstruction of a subset that excludes projections to be corrected. The comparison is done as a 2-D correlation, which serves as a cost function for a simplex-based optimizer which searches for optimal “elevation” angles. The algorithm is called “fixPP” and it was validated by applying to simulated Shepp-Logan phantom data with a 10 degree tilt of the axis of rotation over several projections applied. The left image shows an uncorrected reconstruction; notice degradation of the smaller dark ellipse and the small bright ellipse as compared with the corrected image (right).

3D image analysis and visualization Reconstructed 3D images were analyzed, rendered, and visualized using Amira software (v. 5.6.0, FEI, Hillsboro, Oreg.). 3D object counting and characterization was performed using ImageJ (v. 1.51f, National Institutes of Health, Bethesda, Md., http://imagej.nih.gov/ij).

Discussion

Absolute volumetric data of nuclear and mitochondrial distribution and dynamics have been obtained. Many other commercial fluorophores can be utilized to target other organelles or proteins in live cells. Our experimental findings indicate that a majority of mitochondria in suspension cells are highly interconnected forming a complex and dynamic filamentous network. The quantification of mitochondrial and nuclear remodeling over time enables direct multi-parameter comparisons between individual cells or cell types offering a unique way for rare cell identification with high accuracy. The quantitative data obtained with LCCT is amenable for multiparameter characterization of cellular phenotypes with improved sensitivity and specificity as compared to other, semi-quantitative imaging modalities.

The isotropic 3D spatial resolution provided by LCCT enables absolute measurements at the diffraction limit. Spectrally resolved imaging permits simultaneous detection and correlation of structural and functional alterations in different organelles and/or regions of the cell. The number of spectral channels can be increased by spectrally splitting emission photons onto different quadrants of the detector or using additional detectors. Although here Applicants present only a limited number of morphometric nuclear and mitochondrial features, the set of quantitative features can be expanded markedly by adding a variety of established textural measures to the computational quantification pipeline. This would not only increase the amount of detail obtainable on a single-cell basis, but may also substantially improve statistical power of differentiating cell subtypes. While current spatial resolution of the method is diffraction-limited, it is conceivable that some of the advanced superresolution imaging modalities may be combined to surpass the diffraction limit. Modalities based on single emitter localization (STORM, PALM) or stimulated emission depletion (STED) via engineered PSF may be too slow for imaging of densely stained entire cells and/or damaging to make such a combination biologically reasonable. On the other hand, modalities such as structured illumination microscopy (SIM) or image scanning microscopy (ISM), which both offer about two fold increase in spatial resolution, appear feasible candidates. Due to their wide field image acquisition mode, which is directly compatible with LCCT, both SIM and ISM could be used to acquire projection images with resulting isotropic resolution of 125-150 nm.

The mitochondria of the suspended cells (FIGS. 8 and 9) appear different from what one would typically expect from confocal imaging of mitochondria in cells adhered to a planar substrate. Namely, in adherent cells with most of the mitochondria located in a flattened thin layer of the cytoplasm, one can usually observe individual mitochondria in the shape of elongated string-shaped or round structures. Contrary to separate mitochondria, mitochondria in our images appear distributed around the nucleus and form a complex, dense 3D network partially in form of clusters of varying size. It is possible that this appearance of the mitochondria is due to three not mutually exclusive causes: 1) Mitochondria movement during imaging; 2) Cell type specificity; and/or 3) Imaging immune systems cells in their natural conformation—suspension—, as opposed to adhered to a planar substrate. Our confocal images of the mitochondria in fixed K562 cells revealed a qualitatively similar appearance of the mitochondria as in live cells, suggesting that mitochondrial movement on the time scale of 30 seconds or less in the two studied cell types may be considered as a relatively minor factor in LCCT imaging. Applicants therefore conclude that the observed mitochondrial morphology can be attributed to the cell type specificity and/or the fact that the cells are imaged suspended in 3D space rather than adhered on a planar substrate.

Applicants demonstrated a temporal resolution of our method of ˜30 seconds (time needed to acquire a full set of 2D projections of the cell in one or more embodiments) which is mainly limited by the speed of scanning the objective lens. It is conceivable that cellular events taking place on a second scale, e.g. mitochondrial fusion and fission, are detected as temporal averages resulting in blurring of the rapidly altering structures. Faster, resonant scanner systems can be employed to increase the scan speed 5-fold or more resulting in improved temporal resolution. Depending on the properties of the fluorescent label, photobleaching and resulting phototoxicity may become a limiting factor for time-lapse imaging experiments. Different illumination schemes, such as selective plane illumination or HiLo may be integrated into our system to alleviate the issue of photobleaching and increase imaging contrast.

The various alignment and background correction techniques produced good results that allow region of interest segmentation as required for cancer cell diagnostics. For clinical use, the best possible reconstruction quality must be achieved with sufficiently high throughput to allow feature extraction from a statistically significant number of cells. Some of the more computationally intensive methods would need to be parallelized for fast execution.

The TV method is robust against debris and variations in illumination, but suffers when the cell remains centered in the capillary for the whole rotation. In this situation, where the local background is obscured by the cell in all the projections, the background has to be borrowed from a previous experiment, or the standard flood field can be used as the background.

The more advanced filtering techniques of SAFIR and BM3D have promise for removing the remnants of the background and denoising the images. As shown in FIG. 16, they are excellent at smoothing the images, while retaining fine image detail. However the computationally cheaper TV background combined with a median filter also produces acceptable results (FIG. 15). Adaptive filtering techniques may provide more power in an environment with a higher level of background noise.

This balance between efficacy and computational intensity must be managed for the pseudoprojection registration as well. While the nCOG and tCC methods are clearly better than the COG method, the tCC is far more computationally expensive. Its benefit for the reconstruction is minor when the projections being registered are produced by a high quality cell (properly stained and free from proximal debris). However, in the case of projections with poor signal to noise level, or with obfuscating debris near the cell, the tCC method excels at alignment in the presence of noise. A simple switch within the code can be used to apply the method that will produce the best results.

In summary, the presented method is a powerful and versatile tool for investigating cellular dynamics in cell suspensions in a quantitative fashion by enabling measurements in 3D space with diffraction limited spatial resolution. The approach enables quantitative studies of cellular architecture dynamics and could be utilized for combined nuclear and mitochondrial organization studies in response to treatment. The article, L. Kelbauskas, R. Shetty, and et al., “Optical computed tomography for spatially isotropic four-dimensional imaging of live single cells,” Science Advances, 2017; 3: e1602580, is incorporated herein in its entirety. 

We claim:
 1. A method of acquiring a three dimensional (3D) image of an object, comprising: providing an imaging chamber containing an object and a microscope, wherein the chamber can rotate around a first axis when a focal plane of the microscope scans through the chamber perpendicularly to the first axis; rotating the chamber containing the object; acquiring a two dimensional (2D) pseudoprojection (PP) image of the object during rotation to form a plurality of N (N>1) 2D PP images of the object; masking the location of the object and debris in the plurality of N (N>1) 2D PP images to produce a plurality of masked images; determining an intensity profile of the imaging chamber by forward projecting each projection in the plurality of masked images along the first axis; generating a plurality of M (M=N) refined 2D PP images by subtracting the intensity profile of the imaging chamber from the plurality of N (N>1) 2D PP images; and aligning the plurality of M refined 2D PP images of the object to construct a 3D image of the object.
 2. The method of claim 1, wherein each 2D PP image of the object corresponds to a rotation angle of the object at an angular sampling rate.
 3. The method of claim 2, wherein the angular sampling rate is about 0.72 to about 1.2 degrees.
 4. The method of claim 1, wherein the step of determining the intensity profile further includes taking an average of all the plurality of masked images.
 5. The method of claim 1, wherein the step of generating the plurality of M (M=N) refined 2D PP images further comprises employing a total variation noise reduction (TV) method.
 6. The method of claim 1, wherein the step of generating the plurality of M (M=N) refined 2D PP images further comprises employing a filtering method that is a SAFIR filter.
 7. The method of claim 1, wherein the step of generating the plurality of M (M=N) refined 2D PP images further comprises employing a filtering method that is a block machine filtering (BM3D) filter.
 8. The method of claim 1, wherein the step of masking the location of the object and debris step further comprises applying the Otsu threshold.
 9. The method of claim 1, wherein the object is selected from the group consisting of an individual live cell, individual fixed cell, live multicellular clusters, fixed multicellular clusters, live tissue, or fixed tissue and any combinations thereof.
 10. The method of claim 1, wherein the aligning step further comprises: determining a center of the object on each of the plurality of M refined 2D PP images of the object; determining a size of the object on each of the plurality of M refined 2D PP images of the object; cutting the object projection from each of the plurality of M refined 2D PP images of the object; reconstructing the cut object projection with another cut object projection from a refined forward 2D PP image of the object; and repeating the reconstruction step until generating a 3D model of the object.
 11. A non-transitory computer useable medium encoded with a computer program product to acquire a 3D image of an object and useable with programmable computer processor disposed within a controller in communication with an assembly comprising an imaging chamber containing the object and a microscope, wherein the chamber can rotate around a first axis when a focal plane of the microscope scans through the chamber perpendicularly to the first axis, comprising: computer readable program code that causes said programmable computer processor to rotate the chamber containing the object; computer readable program code that causes said programmable computer processor to acquire a two dimensional (2D) pseudoprojection (PP) image of the object during rotation to form a plurality of N (N>1) 2D PP images of the object; computer readable program code that causes said programmable computer processor to mask the location of the object and debris in the plurality of N (N>1) 2D PP images to produce a plurality of masked images; computer readable program code that causes said programmable computer processor to determine an intensity profile of the imaging chamber by forward projecting each projection in the plurality of masked images along the first axis; computer readable program code that causes said programmable computer processor to generate a plurality of M (M=N) refined 2D PP images by subtracting the intensity profile of the imaging chamber from the plurality of N (N>1) 2D PP images; and computer readable program code that causes said programmable computer processor to align the plurality of M refined 2D PP images of the object to construct a 3D image of the object.
 12. The computer program product of claim 11, wherein each 2D PP image of the object corresponds to a rotation angle of the object at an angular sampling rate.
 13. The computer program product of claim 12, wherein the angular sampling rate is about 0.72 to about 1.2 degrees.
 14. The computer program product of claim 11, further comprising: computer readable program code that causes said programmable computer processor to average of all the plurality of masked images to generate the intensity profile.
 15. The computer program product of claim 11, further comprising computer readable program code that causes said programmable computer processor to employ a total variation noise reduction (TV) method in the step of generating the plurality of M (M=N) refined 2D PP images.
 16. The computer program product of claim 11, further comprising computer readable program code that causes said programmable computer processor to employ a SAFIR filter in the step of generating the plurality of M (M=N) refined 2D PP images.
 17. The computer program product of claim 11, further comprising computer readable program code that causes said programmable computer processor to employ a BM3D filter in the step of generating the plurality of M (M=N) refined 2D PP images.
 18. The computer program product of claim 11, further comprising computer readable program code that causes said programmable computer processor to apply the Otsu threshold during the step of masking the location of the object and debris.
 19. The computer program product of claim 11, wherein the object is selected from the group consisting of an individual live cell, individual fixed cell, live multicellular clusters, fixed multicellular clusters, live tissue, or fixed tissue and any combinations thereof.
 20. The computer program product of claim 11, further comprising: computer readable program code that causes said programmable computer processor to determine a center of the object on each of the plurality of M refined 2D PP images of the object; computer readable program code that causes said programmable computer processor to determine a size of the object on each of the plurality of M refined 2D PP images of the object; computer readable program code that causes said programmable computer processor to cut the object projection from each of the plurality of M refined 2D PP images of the object; computer readable program code that causes said programmable computer processor to reconstruct the cut object projection with another cut object projection from a refined forward 2D PP image of the object; and computer readable program code that causes said programmable computer processor to repeat the reconstruction step until generating a 3D model of the object. 